{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "130d84b5-fe68-4a4f-be53-fd9f01f1b9c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
      "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n",
      "x_train.shape= (25000,)\n",
      "y_train.shape= (25000,)\n",
      "x_test.shape= (25000,)\n",
      "y_test.shape= (25000,)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as  plt\n",
    "imdb=tf.keras.datasets.imdb\n",
    "(x_train,y_train),(x_test,y_test)=imdb.load_data(num_words=4000)\n",
    "print(\"x_train.shape=\",x_train.shape)\n",
    "print(\"y_train.shape=\",y_train.shape)\n",
    "print(\"x_test.shape=\",x_test.shape)\n",
    "print(\"y_test.shape=\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4558a0d1-5d07-411b-a024-3482d5c51e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "序列填充前的第一个元素：\n",
      " [1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 2, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n",
      "序列填充后的第一个元素：\n",
      " [   1   14   22   16   43  530  973 1622 1385   65  458    2   66 3941\n",
      "    4  173   36  256    5   25  100   43  838  112   50  670    2    9\n",
      "   35  480  284    5  150    4  172  112  167    2  336  385   39    4\n",
      "  172    2 1111   17  546   38   13  447    4  192   50   16    6  147\n",
      " 2025   19   14   22    4 1920    2  469    4   22   71   87   12   16\n",
      "   43  530   38   76   15   13 1247    4   22   17  515   17   12   16\n",
      "  626   18    2    5   62  386   12    8  316    8  106    5    4 2223\n",
      "    2   16  480   66 3785   33    4  130   12   16   38  619    5   25\n",
      "  124   51   36  135   48   25 1415   33    6   22   12  215   28   77\n",
      "   52    5   14  407   16   82    2    8    4  107  117    2   15  256\n",
      "    4    2    7 3766    5  723   36   71   43  530  476   26  400  317\n",
      "   46    7    4    2 1029   13  104   88    4  381   15  297   98   32\n",
      " 2071   56   26  141    6  194    2   18    4  226   22   21  134  476\n",
      "   26  480    5  144   30    2   18   51   36   28  224   92   25  104\n",
      "    4  226   65   16   38 1334   88   12   16  283    5   16    2  113\n",
      "  103   32   15   16    2   19  178   32    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0]\n"
     ]
    }
   ],
   "source": [
    "print(\"序列填充前的第一个元素：\\n\",x_train[0])\n",
    "x_train=tf.keras.preprocessing.sequence.pad_sequences(x_train,padding='post',maxlen=400,truncating='post')\n",
    "x_test=tf.keras.preprocessing.sequence.pad_sequences(x_test,padding='post',maxlen=400,truncating='post')\n",
    "print(\"序列填充后的第一个元素：\\n\",x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1fbe29dd-da0b-42e2-9df5-51d29eeea06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)              │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Embedding(output_dim=32,input_dim=4000,input_length=400))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.LSTM(32))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cd912098-9b76-4d89-aa2b-70eb21e240db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 79ms/step - accuracy: 0.5749 - loss: 0.6821 - val_accuracy: 0.6584 - val_loss: 0.6404\n",
      "Epoch 2/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 77ms/step - accuracy: 0.6410 - loss: 0.6458 - val_accuracy: 0.6860 - val_loss: 0.6260\n",
      "Epoch 3/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 77ms/step - accuracy: 0.6475 - loss: 0.6519 - val_accuracy: 0.5268 - val_loss: 0.6329\n",
      "Epoch 4/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.6872 - loss: 0.6088 - val_accuracy: 0.6418 - val_loss: 0.6557\n",
      "Epoch 5/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.7074 - loss: 0.5964 - val_accuracy: 0.5206 - val_loss: 0.7651\n",
      "Epoch 6/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 78ms/step - accuracy: 0.5813 - loss: 0.6628 - val_accuracy: 0.7108 - val_loss: 0.5781\n",
      "Epoch 7/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 79ms/step - accuracy: 0.7416 - loss: 0.5569 - val_accuracy: 0.7218 - val_loss: 0.5745\n",
      "Epoch 8/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 79ms/step - accuracy: 0.7540 - loss: 0.5518 - val_accuracy: 0.7544 - val_loss: 0.5511\n",
      "Epoch 9/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 79ms/step - accuracy: 0.7575 - loss: 0.5389 - val_accuracy: 0.7822 - val_loss: 0.5219\n",
      "Epoch 10/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 79ms/step - accuracy: 0.7778 - loss: 0.5201 - val_accuracy: 0.7214 - val_loss: 0.5752\n",
      "391/391 - 9s - 23ms/step - accuracy: 0.7230 - loss: 0.5716\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.571552038192749, 0.7229599952697754]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "history=model.fit(x_train,y_train,batch_size=64,epochs=10,validation_split=0.2)\n",
    "model.evaluate(x_test,y_test,batch_size=64,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d13ecb03-4225-49e8-bbc6-ea412289f3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss=history.history['loss']\n",
    "acc=history.history['accuracy']\n",
    "val_loss=history.history['val_loss']\n",
    "val_acc=history.history['val_accuracy']\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(loss,color='b',label='train')\n",
    "plt.plot(val_loss,color='r',label='validate')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(acc,color='b',label='train')\n",
    "plt.plot(val_acc,color='r',label='validate')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fd0ceec5-2b40-46e5-baf2-3af23b5a2711",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "评论为： The ultimate story of friendship, of hope, and of life,and overcoming adversity.I understand why so many class this as the bestfilm of all time, it isn't mine, but I get it. If you haven'tseen it, or haven't seen it for some time, you need to watch it, it's amazing.\n",
      "预测结果为： 正面评论\n"
     ]
    }
   ],
   "source": [
    "dict={0:\"正面评论\",1:\"负面评论\"}\n",
    "def display_predict(text):\n",
    "    token=tf.keras.preprocessing.text.Tokenizer(num_words=4000)\n",
    "    token.fit_on_texts(text)\n",
    "    input_seq=token.texts_to_sequences(text)\n",
    "    test_seq=tf.keras.preprocessing.sequence.pad_sequences(input_seq,padding='post',maxlen=400,truncating='post')\n",
    "    pred=model.predict(test_seq)\n",
    "    print(\"评论为：\",text)\n",
    "    print(\"预测结果为：\",dict[np.argmax(pred)])\n",
    "test_text=\"The ultimate story of friendship, of hope, and of life,and overcoming adversity.I understand why so many class this as the bestfilm of all time, it isn't mine, but I get it. If you haven'tseen it, or haven't seen it for some time, you need to watch it, it's amazing.\"\n",
    "display_predict(test_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86de437e-ba90-46e0-b308-ffa647ab416d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c4a8397-742b-4547-b51d-29670306b085",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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